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View all- Majeed AHwang S(2023)The Changing Landscape of Privacy– Countermeasures in the Era of the COVID-19 PandemicIT Professional10.1109/MITP.2023.328787625:4(52-60)Online publication date: 1-Jul-2023
Achieving data privacy for decision support systems in times of massive data sharing
Authors: 
Rabeeha Fazal, Munam Ali Shah, Hasan Ali Khattak, Hafiz Tayyab Rauf, Fadi Al-TurjmanAuthors Info & Claims
Rabeeha Fazal, Munam Ali Shah, Hasan Ali Khattak, Hafiz Tayyab Rauf, Fadi Al-TurjmanAuthors Info & ClaimsAbstract
The world is suffering from a new pandemic of Covid-19 that is affecting human lives. The collection of records for Covid-19 patients is necessary to tackle that situation. The decision support systems (DSS) are used to gather that records. The researchers access the patient’s data through DSS and perform predictions on the severity and effect of the Covid-19 disease; in contrast, unauthorized users can also access the data for malicious purposes. For that reason, it is a challenging task to protect Covid-19 patient data. In this paper, we proposed a new technique for protecting Covid-19 patients’ data. The proposed model consists of two folds. Firstly, Blowfish encryption uses to encrypt the identity attributes. Secondly, it uses Pseudonymization to mask identity and quasi-attributes, then all the data links with one another, such as the encrypted, masked, sensitive, and non-sensitive attributes. In this way, the data becomes more secure from unauthorized access.
References
[1]
Govindan K, Mina H, and Alavi B A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: a case study of coronavirus disease 2019 (COVID-19) Transp. Res. E 2020 138 101967
[2]
Afzal, M., Hussain, M., Lee, S., Khattak, H.A.: Redesign of clinical decision systems to support precision medicine. In: TENCON 2018–2018 IEEE region 10 conference, pp. 2259–2263. IEEE (2018)
[3]
Khattak HA, Imran M, Abbas A, and Khan SU Maintaining fog trust through continuous assessment, in world congress on services 2019 Cham Springer 129-137
[4]
Ahmad I, Shah MA, Khattak HA, Ameer Z, Khan M, and Han K FIViz: forensics investigation through visualization for malware in internet of things Sustainability 2020 12 18 7262
[5]
Gheisari M, Najafabadi HE, Alzubi JA, Gao J, Wang G, Abbasi AA, and Castiglione A OBPP: an ontology-based framework for privacy-preserving in IoT-based smart city Future Gener. Comput. Syst. 2021 123 1
[6]
Lenert L and McSwain BY Balancing health privacy, health information exchange, and research in the context of the COVID-19 pandemic J. Am. Med. Inform. Assoc. 2020 27 6 963
[7]
Zwitter A and Gstrein OJ Big data, Big data, privacy and COVID-19–learning from humanitarian expertise in data protection J. Int. Hum. Action 2020 5 1 4
[8]
Ekong I, Chukwu E, and Chukwu M COVID-19 mobile positioning data contact tracing and patient privacy regulations: exploratory search of global response strategies and the use of digital tools in Nigeria JMIR mHealth uHealth 2020 8 4 e19139
[9]
Kim W, Lee H, and Chung YD Safe contact tracing for COVID-19: a method without privacy breach using functional encryption techniques based-on spatio-temporal trajectory data PLoS ONE 2020 15 12 e0242758
[10]
Asghar A, Abbas A, Khattak HA, and Khan SU Fog based architecture and load balancing methodology for health monitoring systems IEEE Access 2021 9 96189
[11]
Sun HM, Chen YH, and Lin YH oPass: a user authentication protocol resistant to password stealing and password reuse attacks IEEE Trans. Inform. Forens. Secur. 2011 7 2 651
[12]
Ratna, A.A.P., Purnamasari, P.D., Shaugi, A., Salman, M.: Analysis and comparison of MD5 and SHA-1 algorithm implementation in Simple-O authentication based security system. In: 2013 international conference on QiR, pp. 99–104, IEEE, (2013)
[13]
Rama, M., Raja, S.S.: Web based security analysis of OPASS authentication schemes using mobile application. In: 2013 international conference on emerging trends in VLSI, embedded system, nano electronics and telecommunication system (ICEVENT), pp. 1–3, IEEE, (2013)
[14]
Sirohi, P., Agarwal, A., Tyagi, S.: A comprehensive study on security attacks on SSL/TLS protocol. In: 2016 2nd international conference on next generation computing technologies (NGCT), pp. 893–898, IEEE, (2016)
[15]
Kogetsu A, Ogishima S, and Kato K Authentication of patients and participants in health information exchange and consent for medical research: a key step for privacy protection, respect for autonomy, and trustworthiness Front. Genet. 2018 9 167
[16]
Kansuwan, T., Chomsiri, T.: Authentication model using the bundled CAPTCHA OTP instead of traditional password. In: 2019 joint international conference on digital arts, media and technology with ECTI northern section conference on electrical, electronics, computer and telecommunications engineering (ECTI DAMT-NCON), pp. 5–8, IEEE, (2019)
[17]
Rahardja U, Bist AS, Hardini M, Aini Q, and Harahap EP Authentication of Covid-19 patient certification with blockchain protocol Int. J. Adv. Sci Technol. 2020 29 8s 4015
[18]
Rauf HT, Malik S, Shoaib U, Irfan MN, and Lali MI Adaptive inertia weight bat algorithm with Sugeno-function fuzzy search Appl. Soft Comput. 2020 90 106159
[19]
Gao, J., Wang, H., Shen, H.: Smartly handling renewable energy instability in supporting a cloud datacenter. In: 2020 IEEE international parallel and distributed processing symposium (IPDPS), pp. 769–778, IEEE, (2020)
[20]
Alanazi, H., Zaidan, B.B., Zaidan, A.A., Jalab, H.A., Shabbir, M., Al-Nabhani, Y., et al.: New comparative study between DES, 3DES and AES within nine factors, arXiv preprint arXiv:1003.4085 (2010)
[21]
Gao J, Wang H, and Shen H Task failure prediction in cloud data centers using deep learning IEEE Trans. Serv. Comput. 2020
[22]
Gao, J., Wang, H., Shen, H.: Machine learning based workload prediction in cloud computing. In: 2020 29th international conference on computer communications and networks (ICCCN), pp. 1–9, IEEE, (2020)
[23]
Aggarwal K, Saini JK, and Verma HK Performance evaluation of RC6, blowfish, DES, IDEA, CAST-128 block ciphers Int. J. Comput. Appl. 2013 68 25 10-16
[24]
Singh P and Kumar S Study and analysis of cryptography algorithms: RSA, AES, DES, T-DES, blowfish Int. J. Eng. Technol. 2017 7 1.5 221
[25]
Wahid MNA, Ali A, Esparham B, and Marwan M A comparison of cryptographic algorithms: DES, 3DES, AES, RSA and blowfish for guessing attacks prevention J. Comput. Sci. Appl. Inform. Technol. 2018 3 2 1
[26]
Patel K Performance analysis of AES, DES and blowfish cryptographic algorithms on small and large data files Int. J. Inform. Technol. 2019 11 4 813
[27]
Commey D, Griffith S, and Dzisi J Performance comparison of 3DES AES, blowfish and RSA for dataset classification and encryption in cloud data storage Int. J. Comput. Appl. 2020 177 40 17-22
[28]
Lucca AV, Luchtenberg R, de Paula Conceicao LG, Silva LA, Ovejero RG, Navarro-Cáceres M, and Leithardt VRQ System for control and management of data privacy of patients with COVID-19 Europe PMC 2020 20 1 1-19
[29]
Tahir M, Sardaraz M, Mehmood Z, and Muhammad S CryptoGA: a cryptosystem based on genetic algorithm for cloud data security Clust. Comput. 2020 24 739-752
[30]
Hussaini S Cyber security in cloud using blowfish encryption Int. J. Inform. Technol. (IJIT) 2020 6 5 13-19
[31]
Parkar AP, Gedam MN, Ansari N, and Therese S Ann R Performance level evaluation of cryptographic algorithms Intelligent computing and networking 2021 Singapore Springer 157-167
[32]
Chao, Y., Bingyao, C., Jiaying, D., Wei, G.: The research and implementation of UTM. In: IET international communication conference on wireless mobile and computing (CCWMC 2009), pp. 389–392, IET, (2009)
[33]
Wang L Big data in intrusion detection systems and intrusion prevention systems J. Comput. Netw. 2017 4 1 48
[34]
Abouelmehdi K, Beni-Hessane A, and Khaloufi H Big healthcare data: preserving security and privacy J. Big Data 2018 5 1 1
[35]
Awan KA, Din IU, Almogren A, Khattak HA, and Rodrigues JJ EdgeTrust-a lightweight data-centric trust management approach for green internet of edge things Wirel. Pers. Commun. 2021
[36]
Neupane, K., Haddad, R., Chen, L.: Next generation firewall for network security: a survey. In: SoutheastCon 2018, pp. 1–6, IEEE, (2018)
[37]
Liang, D.: Security information and event management, security information and event management. US Patent 10,616,258. (2020)
[38]
Thapa C and Camtepe S Precision health data: requirements, challenges and existing techniques for data security and privacy Comput. Biol. Med. 2020 129 104130
[39]
Bernier A and Knoppers BM Pandemics, privacy, and public health research Can. J. Public Health 2020 111 4 454
[40]
Prince PB and Lovesum SJ Privacy enforced access control model for secured data handling in cloud-based pervasive health care system SN Comput. Sci. 2020 1 5 1
[41]
AlMarzooqi FM, Moonesar IA, and AlQutob R Healthcare professional and user perceptions of eHealth data and record privacy in Dubai Information 2020 11 9 415
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© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.
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Kluwer Academic Publishers
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Publication History
Published: 01 October 2022
Accepted: 06 December 2021
Revision received: 30 November 2021
Received: 01 May 2021
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View all- Majeed AHwang S(2023)The Changing Landscape of Privacy– Countermeasures in the Era of the COVID-19 PandemicIT Professional10.1109/MITP.2023.328787625:4(52-60)Online publication date: 1-Jul-2023
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